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fix: update g3doc links #2273

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Apr 15, 2024
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fix: update g3doc links #2273

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6bb1b14
fix: update g3doc links
emmanuel-ferdman Sep 25, 2023
0e20971
Update site/en/hub/migration_tf2.md
8bitmp3 Feb 12, 2024
9eee5f9
Merge branch 'tensorflow:master' into wip
emmanuel-ferdman Apr 3, 2024
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2 changes: 1 addition & 1 deletion site/en/hub/common_saved_model_apis/images.md
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Expand Up @@ -70,7 +70,7 @@ consumer. The SavedModel itself should not perform dropout on the actual outputs
Reusable SavedModels for image feature vectors are used in

* the Colab tutorial
[Retraining an Image Classifier](https://colab.research.google.com/github/tensorflow/docs/blob/master/g3doc/en/hub/tutorials/tf2_image_retraining.ipynb),
[Retraining an Image Classifier](https://colab.research.google.com/github/tensorflow/docs/blob/master/site/en/hub/tutorials/tf2_image_retraining.ipynb),

<a name="classification"></a>

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2 changes: 1 addition & 1 deletion site/en/hub/common_saved_model_apis/text.md
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Expand Up @@ -94,7 +94,7 @@ distributed way. For example
### Examples

* Colab tutorial
[Text Classification with Movie Reviews](https://colab.research.google.com/github/tensorflow/docs/blob/master/g3doc/en/hub/tutorials/tf2_text_classification.ipynb).
[Text Classification with Movie Reviews](https://colab.research.google.com/github/tensorflow/docs/blob/master/site/en/hub/tutorials/tf2_text_classification.ipynb).

<a name="text-embeddings-preprocessed"></a>

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4 changes: 2 additions & 2 deletions site/en/hub/installation.md
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Expand Up @@ -50,8 +50,8 @@ $ pip install --upgrade tf-hub-nightly

- [Library overview](lib_overview.md)
- Tutorials:
- [Text classification](https://github.com/tensorflow/docs/blob/master/g3doc/en/hub/tutorials/tf2_text_classification.ipynb)
- [Image classification](https://github.com/tensorflow/docs/blob/master/g3doc/en/hub/tutorials/tf2_image_retraining.ipynb)
- [Text classification](https://github.com/tensorflow/docs/blob/master/site/en/hub/tutorials/tf2_text_classification.ipynb)
- [Image classification](https://github.com/tensorflow/docs/blob/master/site/en/hub/tutorials/tf2_image_retraining.ipynb)
- Additional examples
[on GitHub](https://github.com/tensorflow/hub/blob/master/examples/README.md)
- Find models on [tfhub.dev](https://tfhub.dev).
4 changes: 2 additions & 2 deletions site/en/hub/migration_tf2.md
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Expand Up @@ -48,8 +48,8 @@ model = tf.keras.Sequential([

Many tutorials show these APIs in action. See in particular
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* [Text classification example notebook](https://github.com/tensorflow/docs/blob/master/g3doc/en/hub/tutorials/tf2_text_classification.ipynb)
* [Image classification example notebook](https://github.com/tensorflow/docs/blob/master/g3doc/en/hub/tutorials/tf2_image_retraining.ipynb)
* [Text classification example notebook](https://github.com/tensorflow/docs/blob/master/site/en/hub/tutorials/tf2_text_classification.ipynb)
* [Image classification example notebook](https://github.com/tensorflow/docs/blob/master/site/en/hub/tutorials/tf2_image_retraining.ipynb)

### Using the new API in Estimator training

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4 changes: 2 additions & 2 deletions site/en/hub/tf2_saved_model.md
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Expand Up @@ -51,7 +51,7 @@ model = tf.keras.Sequential([
```

The [Text classification
colab](https://colab.research.google.com/github/tensorflow/docs/blob/master/g3doc/en/hub/tutorials/tf2_text_classification.ipynb)
colab](https://colab.research.google.com/github/tensorflow/docs/blob/master/site/en/hub/tutorials/tf2_text_classification.ipynb)
is a complete example how to train and evaluate such a classifier.

The model weights in a `hub.KerasLayer` are set to non-trainable by default.
Expand Down Expand Up @@ -244,7 +244,7 @@ to the Keras model, and runs the SavedModel's computation in training
mode (think of dropout etc.).

The [image classification
colab](https://github.com/tensorflow/docs/blob/master/g3doc/en/hub/tutorials/tf2_image_retraining.ipynb)
colab](https://github.com/tensorflow/docs/blob/master/site/en/hub/tutorials/tf2_image_retraining.ipynb)
contains an end-to-end example with optional fine-tuning.

#### Re-exporting the fine-tuning result
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14 changes: 7 additions & 7 deletions site/en/hub/tutorials/text_cookbook.md
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Expand Up @@ -34,7 +34,7 @@ library for tokenization and preprocessing.

### Kaggle

[IMDB classification on Kaggle](https://github.com/tensorflow/docs/blob/master/g3doc/en/hub/tutorials/text_classification_with_tf_hub_on_kaggle.ipynb) -
[IMDB classification on Kaggle](https://github.com/tensorflow/docs/blob/master/site/en/hub/tutorials/text_classification_with_tf_hub_on_kaggle.ipynb) -
shows how to easily interact with a Kaggle competition from a Colab, including
downloading the data and submitting the results.

Expand All @@ -43,14 +43,14 @@ downloading the data and submitting the results.
[Text classification](https://www.tensorflow.org/hub/tutorials/text_classification_with_tf_hub) | ![done](https://www.gstatic.com/images/icons/material/system_gm/1x/bigtop_done_googblue_18dp.png) | | | | |
[Text classification with Keras](https://www.tensorflow.org/tutorials/keras/text_classification_with_hub) | | ![done](https://www.gstatic.com/images/icons/material/system_gm/1x/bigtop_done_googblue_18dp.png) | ![done](https://www.gstatic.com/images/icons/material/system_gm/1x/bigtop_done_googblue_18dp.png) | ![done](https://www.gstatic.com/images/icons/material/system_gm/1x/bigtop_done_googblue_18dp.png) | |
[Predicting Movie Review Sentiment with BERT on TF Hub](https://github.com/google-research/bert/blob/master/predicting_movie_reviews_with_bert_on_tf_hub.ipynb) | ![done](https://www.gstatic.com/images/icons/material/system_gm/1x/bigtop_done_googblue_18dp.png) | | | | ![done](https://www.gstatic.com/images/icons/material/system_gm/1x/bigtop_done_googblue_18dp.png) |
[IMDB classification on Kaggle](https://github.com/tensorflow/docs/blob/master/g3doc/en/hub/tutorials/text_classification_with_tf_hub_on_kaggle.ipynb) | ![done](https://www.gstatic.com/images/icons/material/system_gm/1x/bigtop_done_googblue_18dp.png) | | | | | ![done](https://www.gstatic.com/images/icons/material/system_gm/1x/bigtop_done_googblue_18dp.png)
[IMDB classification on Kaggle](https://github.com/tensorflow/docs/blob/master/site/en/hub/tutorials/text_classification_with_tf_hub_on_kaggle.ipynb) | ![done](https://www.gstatic.com/images/icons/material/system_gm/1x/bigtop_done_googblue_18dp.png) | | | | | ![done](https://www.gstatic.com/images/icons/material/system_gm/1x/bigtop_done_googblue_18dp.png)

### Bangla task with FastText embeddings
TensorFlow Hub does not currently offer a module in every language. The
following tutorial shows how to leverage TensorFlow Hub for fast experimentation
and modular ML development.

[Bangla Article Classifier](https://github.com/tensorflow/docs/blob/master/g3doc/en/hub/tutorials/bangla_article_classifier.ipynb) -
[Bangla Article Classifier](https://github.com/tensorflow/docs/blob/master/site/en/hub/tutorials/bangla_article_classifier.ipynb) -
demonstrates how to create a reusable TensorFlow Hub text embedding, and use it
to train a Keras classifier for
[BARD Bangla Article dataset](https://github.com/tanvirfahim15/BARD-Bangla-Article-Classifier).
Expand All @@ -64,24 +64,24 @@ setup (no training examples).

### Basic

[Semantic similarity](https://github.com/tensorflow/docs/blob/master/g3doc/en/hub/tutorials/semantic_similarity_with_tf_hub_universal_encoder.ipynb) -
[Semantic similarity](https://github.com/tensorflow/docs/blob/master/site/en/hub/tutorials/semantic_similarity_with_tf_hub_universal_encoder.ipynb) -
shows how to use the sentence encoder module to compute sentence similarity.

### Cross-lingual

[Cross-lingual semantic similarity](https://github.com/tensorflow/docs/blob/master/g3doc/en/hub/tutorials/cross_lingual_similarity_with_tf_hub_multilingual_universal_encoder.ipynb) -
[Cross-lingual semantic similarity](https://github.com/tensorflow/docs/blob/master/site/en/hub/tutorials/cross_lingual_similarity_with_tf_hub_multilingual_universal_encoder.ipynb) -
shows how to use one of the cross-lingual sentence encoders to compute sentence
similarity across languages.

### Semantic retrieval

[Semantic retrieval](https://github.com/tensorflow/docs/blob/master/g3doc/en/hub/tutorials/retrieval_with_tf_hub_universal_encoder_qa.ipynb) -
[Semantic retrieval](https://github.com/tensorflow/docs/blob/master/site/en/hub/tutorials/retrieval_with_tf_hub_universal_encoder_qa.ipynb) -
shows how to use Q/A sentence encoder to index a collection of documents for
retrieval based on semantic similarity.

### SentencePiece input

[Semantic similarity with universal encoder lite](https://github.com/tensorflow/docs/blob/master/g3doc/en/hub/tutorials/semantic_similarity_with_tf_hub_universal_encoder_lite.ipynb) -
[Semantic similarity with universal encoder lite](https://github.com/tensorflow/docs/blob/master/site/en/hub/tutorials/semantic_similarity_with_tf_hub_universal_encoder_lite.ipynb) -
shows how to use sentence encoder modules that accept
[SentencePiece](https://github.com/google/sentencepiece) ids on input instead of
text.
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